The non-genomic effects of the PPARβγ agonist GW0742 on streptozotocin treated rat aorta

Copyright© Bentham Science Publishers; For any queries, please email at [email protected]: The ubiquitous nuclear receptor PPARβ/δ is increasingly being studied in regards to numerous diseases including diabetes following on the finding that PPARβ/δ agonist GW0742 controls Type 1 Diabetes in rats. Studies have shown that GW0742 has off target, non- PPARβ/δ effects in the cell although there are some key questions that remain to be addressed in respect to the significance of this control on vascular tone. Methods: Using isometric organ baths, rat aorta rings were exposed to ROCK inhibitors and the changes in contraction and dilation measured. Results: Our data shows that the PPARβ/δ agonist GW0742 (10 -7M) inhibits contractile responses to U46619 and phenylephrine, and that these responses are similar in normal and Streptozotocin (STZ) diabetic rat aorta. ROCK inhibitors Fasudil and Y27632 significantly reduced GW0742 mediated dilation of naïve rat aorta, but Fasudil had no effect on GW0742 dilation in STZ diabetic rat aorta. In contrast, STZ diabetic rat aorta pre-contracted with high [K +] Krebs lacked a dilatory response to GW0742, which taken together indicates that the mechanism of action of GW0742 mediated dilation changes in the diabetic state compared to non-diabetic state. Conclusion: This is the first direct evidence demonstrating the non- PPARβ/δ effect of GW0742 on contraction is irrespective to the diabetic state, and that GW0742 has the potential to induce vasodilation via multiple off-target mechanisms.Peer reviewedFinal Accepted Versio

Damned if they do, damned if they don't: negotiating the tricky context of anti-social behaviour and keeping safe in disadvantaged urban neighbourhoods

Young people's relationship with anti-social behaviour (ASB) is complicated. While their behaviours are often stereotyped as anti-social (e.g. ‘hanging about’), they also experience ASB in their neighbourhood. In this study, we explore young people's own perspectives on ASB, comparing results from ‘go-along’ interviews and focus groups conducted in disadvantaged neighbourhoods in Glasgow, Scotland. This article discusses how young people's everyday experience of ASB was contextualised by social factors such as cultural stereotyping of marginalised groups, poor social connectivity and spatial marginalisation within their neighbourhood. Furthermore, we found that these social factors were mutually reinforcing and interacted in a way that appeared to leave young people in a ‘no-win’ situation regarding their association with ASB. Participation in ASB and attempts to avoid such involvement were seen to involve negative consequences: participation could entail violence and spatial restrictions linked to territoriality, but avoidance could lead to being ostracised from their peer group. Regardless of involvement, young people felt that adults stereotyped them as anti-social. Our findings therefore provide support for policies and interventions aimed at reducing ASB (perpetrated by residents of all ages); in part by better ensuring that young people have a clear incentive for avoiding such behaviours

LUCAS: A highly accurate yet simple risk calculator that predicts survival of COVID-19 patients using rapid routine tests

Background There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/. What is already known on this topic? Clinical prediction models such as NEWS2 is currently used in practice as mortality risk assessment. In a rapid response to support COVID-19 patient assessment and resource management, published risk tools and models have been found to have a high risk of bias and therefore cannot be translated into clinical practice. What this study adds? A newly developed and validated risk tool (LUCAS) based on rapid and routine blood tests predicts the mortality of patients infected with SARS-CoV-2 virus. This prediction model has both high and robust predictive power and has been tested on an external set of patients and therefore can be used to effectively triage patients when resources are limited. In addition, LUCAS can be used with chest imaging information and NEWS2 score

A New NO-Releasing Nanoformulation for the Treatment of Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) is a chronicand progressive disease which continues to carry an unacceptablyhigh mortality and morbidity. The nitric oxide (NO) pathwayhas been implicated in the pathophysiology and progressionof the disease. Its extremely short half-life and systemiceffects have hampered the clinical use of NO in PAH. In anattempt to circumvent these major limitations, we have developeda new NO-nanomedicine formulation. The formulationwas based on hydrogel-like polymeric composite NO-releasingnanoparticles (NO-RP). The kinetics of NO release fromthe NO-RP showed a peak at about 120 min followed by asustained release for over 8 h. The NO-RP did not affect theviability or inflammation responses of endothelial cells. TheNO-RP produced concentration-dependent relaxations of pulmonaryarteries in mice with PAH induced by hypoxia. Inconclusion, NO-RP drugs could considerably enhance thetherapeutic potential of NO therapy for PAH

Evidence that links loss of cyclooxygenase-2 with increased asymmetric dimethylarginine : novel explanation of cardiovascular side effects associated with anti-inflammatory drugs

© 2014 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited.BACKGROUND: Cardiovascular side effects associated with cyclooxygenase-2 inhibitor drugs dominate clinical concern. Cyclooxygenase-2 is expressed in the renal medulla where inhibition causes fluid retention and increased blood pressure. However, the mechanisms linking cyclooxygenase-2 inhibition and cardiovascular events are unknown and no biomarkers have been identified.METHODS AND RESULTS: Transcriptome analysis of wild-type and cyclooxygenase-2(-/-) mouse tissues revealed 1 gene altered in the heart and aorta, but >1000 genes altered in the renal medulla, including those regulating the endogenous nitric oxide synthase inhibitors asymmetrical dimethylarginine (ADMA) and monomethyl-l-arginine. Cyclo-oxygenase-2(-/-) mice had increased plasma levels of ADMA and monomethyl-l-arginine and reduced endothelial nitric oxide responses. These genes and methylarginines were not similarly altered in mice lacking prostacyclin receptors. Wild-type mice or human volunteers taking cyclooxygenase-2 inhibitors also showed increased plasma ADMA. Endothelial nitric oxide is cardio-protective, reducing thrombosis and atherosclerosis. Consequently, increased ADMA is associated with cardiovascular disease. Thus, our study identifies ADMA as a biomarker and mechanistic bridge between renal cyclooxygenase-2 inhibition and systemic vascular dysfunction with nonsteroidal anti-inflammatory drug usage.CONCLUSIONS: We identify the endogenous endothelial nitric oxide synthase inhibitor ADMA as a biomarker and mechanistic bridge between renal cyclooxygenase-2 inhibition and systemic vascular dysfunction.Peer reviewedFinal Published versio

A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest x-rays

There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive patients with diverse levels of complexity. Here we presented a simplified risk-tool based on minimal parameters and chest X-ray (CXR) image data that predicts the survival of adult SARS-CoV-2 positive patients at hospital admission. We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS-CoV-2 positive patients, respectively. External validation of the final model was conducted on 741 Accident and Emergency (A&E) admissions with suspected SARS-CoV-2 infection from a separate NHS Trust. The LUCAS mortality score included five strongest predictors (Lymphocyte count, Urea, C-reactive protein, Age, Sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the area under the receiving operating characteristics curve (AUC-ROC) in development cohort 0.765 (95% confidence interval (CI): 0.738–0.790), in internal validation cohort 0.744 (CI: 0.673–0.808), and in external validation cohort 0.752 (CI: 0.713–0.787). The discriminatory power of LUCAS increased slightly when including the CXR image data. LUCAS can be used to obtain valid predictions of mortality in patients within 60 days of SARS-CoV-2 RT-PCR results into low, moderate, high, or very high risk of fatality

Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited

A New NO-Releasing Nanoformulation for the Treatment of Pulmonary Arterial Hypertension

Copyright The Author(s) 2016. This article is published with open access at Springerlink.com. Open Access - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were madePulmonary arterial hypertension (PAH) is a chronic and progressive disease which continues to carry an unacceptably high mortality and morbidity. The nitric oxide (NO) pathway has been implicated in the pathophysiology and progression of the disease. Its extremely short half-life and systemic effects have hampered the clinical use of NO in PAH. In an attempt to circumvent these major limitations, we have developed a new NO-nanomedicine formulation. The formulation was based on hydrogel-like polymeric composite NO-releasing nanoparticles (NO-RP). The kinetics of NO release from the NO-RP showed a peak at about 120 min followed by a sustained release for over 8 h. The NO-RP did not affect the viability or inflammation responses of endothelial cells. The NO-RP produced concentration-dependent relaxations of pulmonary arteries in mice with PAH induced by hypoxia. In conclusion, NO-RP drugs could considerably enhance the therapeutic potential of NO therapy for PAH.Peer reviewedFinal Published versio